

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=sys.maxsize						)		->       Any:
														A      :					List[Any]      		=					'''bilinear'''
														A      :					List[str]      		=					max_size
														A      :					str      		=					short_edge_length






							def __call__(					self    ,						__UpperCAmelCase						)		->       str:
														A      :					Tuple      		=					[]
														for img in imgs:
																					A		,		A      :					str      		=					img.shape[:2]
																					# later: provide list and randomly choose index for resize
																					A      :					Optional[int]      		=					np.random.randint(self.short_edge_length[0]    ,						self.short_edge_length[1] + 1						)
																					if size == 0:
																												return img
																					A      :					Union[str, Any]      		=					size * 1.0 / min(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					if h < w:
																												A		,		A      :					Union[str, Any]      		=					size, scale * w
																					else:
																												A		,		A      :					List[Any]      		=					scale * h, size
																					if max(__UpperCAmelCase    ,						__UpperCAmelCase						) > self.max_size:
																												A      :					Optional[Any]      		=					self.max_size * 1.0 / max(__UpperCAmelCase    ,						__UpperCAmelCase						)
																												A      :					Optional[Any]      		=					newh * scale
																												A      :					Optional[int]      		=					neww * scale
																					A      :					List[Any]      		=					int(neww + 0.5						)
																					A      :					Tuple      		=					int(newh + 0.5						)

																					if img.dtype == np.uinta:
																												A      :					List[Any]      		=					Image.fromarray(__UpperCAmelCase						)
																												A      :					Union[str, Any]      		=					pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR						)
																												A      :					Union[str, Any]      		=					np.asarray(__UpperCAmelCase						)
																					else:
																												A      :					Dict      		=					img.permute(2    ,						0    ,						1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																												A      :					Dict      		=					nn.functional.interpolate(
																												    __UpperCAmelCase    ,						(newh, neww)    ,						mode=self.interp_method    ,						align_corners=__UpperCAmelCase						).squeeze(0						)
																					img_augs.append(__UpperCAmelCase						)

														return img_augs







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														A      :					Tuple      		=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,						cfg.INPUT.MAX_SIZE_TEST						)
														A      :					Optional[int]      		=					cfg.INPUT.FORMAT
														A      :					Union[str, Any]      		=					cfg.SIZE_DIVISIBILITY
														A      :					List[Any]      		=					cfg.PAD_VALUE
														A      :					str      		=					cfg.INPUT.MAX_SIZE_TEST
														A      :					Optional[int]      		=					cfg.MODEL.DEVICE
														A      :					List[Any]      		=					torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
														A      :					Optional[Any]      		=					torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
														A      :					Tuple      		=					lambda __UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					Optional[int]      		=					tuple(max(__UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														A      :					Optional[Any]      		=					[im.shape[-2:] for im in images]
														A      :					Optional[Any]      		=					[
														    nn.functional.pad(
														        __UpperCAmelCase    ,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,						value=self.pad_value    ,						)
														    for size, im in zip(__UpperCAmelCase    ,						__UpperCAmelCase						)
														]

														return torch.stack(__UpperCAmelCase						), torch.tensor(__UpperCAmelCase						)






							def __call__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=False						)		->       Tuple:
														with torch.no_grad():
																					if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												A      :					List[str]      		=					[images]
																					if single_image:
																												assert len(__UpperCAmelCase						) == 1
																					for i in range(len(__UpperCAmelCase						)						):
																												if isinstance(images[i]    ,						torch.Tensor						):
																																			images.insert(__UpperCAmelCase    ,						images.pop(__UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i]    ,						torch.Tensor						):
																																			images.insert(
																																			    __UpperCAmelCase    ,						torch.as_tensor(img_tensorize(images.pop(__UpperCAmelCase						)    ,						input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float()    ,						)
            # resize smallest edge
																					A      :					Any      		=					torch.tensor([im.shape[:2] for im in images]						)
																					A      :					Tuple      		=					self.aug(__UpperCAmelCase						)
																					# transpose images and convert to torch tensors
																					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																					# now normalize before pad to avoid useless arithmetic
																					A      :					Optional[int]      		=					[self.normalizer(__UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					A		,		A      :					List[str]      		=					self.pad(__UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					A      :					Optional[Any]      		=					torch.true_divide(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							assert torch.isfinite(lowerCamelCase_		).all(), "Box tensor contains infinite or NaN!"
							A		,		A      :					List[Any]      		=					box_size
							tensor[:, 0].clamp_(min=0     ,				max=lowerCamelCase_		)
							tensor[:, 1].clamp_(min=0     ,				max=lowerCamelCase_		)
							tensor[:, 2].clamp_(min=0     ,				max=lowerCamelCase_		)
							tensor[:, 3].clamp_(min=0     ,				max=lowerCamelCase_		)









def        snake_case__		(    lowerCamelCase_ = 1000		):
							return sum(e for e in range(3     ,				lowerCamelCase_		) if e % 3 == 0 or e % 5 == 0		)


if __name__ == "__main__":
		print(F"{solution() = }")






